{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating AnyScript Macros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AnyPyTools can create AnyScript macros automatically. Doing so simplifies the process of writing complex macros and makes it easier to do things like parameter studies, Monte Carlo simulation, etc.  \n",
    "\n",
    "There is a class for every macro command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anypytools.macro_commands import (MacroCommand, Load, SetValue, SetValue_random,  Dump, SaveDesign,\n",
    "                                       LoadDesign, SaveValues, LoadValues, UpdateValues, OperationRun)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A quick example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following shows how the to generate a simple macro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[load \"Knee.any\" -def SUBJECT=---\"\\\"S02\\\"\" -def TRIAL=---\"\\\"T04\\\"\",\n",
       " operation Main.MyStudy.InverseDynamics\n",
       " run,\n",
       " classoperation Main.MyStudy.Output.MaxMuscleActivity \"Dump\"]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macrolist = [\n",
    "    Load('Knee.any', defs={'SUBJECT':'\"S02\"', 'TRIAL':'\"T04\"'}),\n",
    "    OperationRun('Main.MyStudy.InverseDynamics'),\n",
    "    Dump('Main.MyStudy.Output.MaxMuscleActivity'),\n",
    "] \n",
    "macrolist "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each macro object will generate the macro commands with the correct syntax.\n",
    "The macro can be launched using the `start_macro()` method of  the `AnyPyProcess`  object.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5940522ac2ac4f4498362576316f72e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: 1\n"
     ]
    }
   ],
   "source": [
    "from anypytools import AnyPyProcess\n",
    "app = AnyPyProcess()\n",
    "app.start_macro(macrolist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview of macro commands\n",
    "The `macro_commands` module have classes for generating many of the standard AnyScipt macro commands.\n",
    "\n",
    "- `Load(mainfile, defines, paths)`: load command\n",
    "- `OperationRun(var)`: select operation and run  \n",
    "- `Dump(var)`: classoperation \"Dump\"\n",
    "- `LoadDesign(var, filename)`: classoperation \"Load design\" \n",
    "- `SaveDesign(var, filename)`: classoperation \"Save design\" \n",
    "- `LoadValues(filename)`: classoperation Main \"Load Values\" \n",
    "- `SaveValues(filename)`: classoperation Main \"Save Values\"\n",
    "- `UpdateValues()`: classoperation \"Update Values\"\n",
    "- `SetValue(var,value)`: classoperation \"Set Value\" \n",
    "- `MacroCommand(macro_string)`: Add abitrary macro string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating many macros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The macro in the previous example would have been easy to write manually. However, in some cases we want to create many macros. Then it is a big advantage to generate them programmatically. \n",
    "\n",
    "To generate many macros we need an extra class `AnyMacro` to wrap our macro list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['load \"Knee.any\"', 'operation Main.MyStudy.InverseDynamics', 'run']]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from anypytools import AnyMacro\n",
    "\n",
    "macrolist = [\n",
    "    Load('Knee.any' ),\n",
    "    OperationRun('Main.MyStudy.InverseDynamics'),\n",
    "] \n",
    "\n",
    "mg = AnyMacro(macrolist)\n",
    "mg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `AnyMacro` just behaves as a container for our macro. But has additional attributes that specify how many macros we want. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['load \"Knee.any\"', 'operation Main.MyStudy.InverseDynamics', 'run'],\n",
       " ['load \"Knee.any\"', 'operation Main.MyStudy.InverseDynamics', 'run'],\n",
       " ['load \"Knee.any\"', 'operation Main.MyStudy.InverseDynamics', 'run'],\n",
       " ['load \"Knee.any\"', 'operation Main.MyStudy.InverseDynamics', 'run'],\n",
       " ['load \"Knee.any\"', 'operation Main.MyStudy.InverseDynamics', 'run']]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg = AnyMacro(macrolist, number_of_macros = 5)\n",
    "mg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can also be overidden when calling its `create_macros()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['load \"Knee.any\"', 'operation Main.MyStudy.InverseDynamics', 'run'],\n",
       " ['load \"Knee.any\"', 'operation Main.MyStudy.InverseDynamics', 'run']]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg.create_macros(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list of macros can also be passed to the 'start_macro' function to executed in parallel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ecadd240db804134983e5cb348f2bd9c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: 100\n"
     ]
    }
   ],
   "source": [
    "from anypytools import AnyPyProcess\n",
    "\n",
    "app = AnyPyProcess()\n",
    "output = app.start_macro(mg.create_macros(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running many macros is only really useful if the macros are different. Some macros classes, like `SetValue()`, accepts lists of values which it distributes across the generated macros. \n",
    "\n",
    "Imagine a list of 5 parameters. We want to create five macros that use these values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['classoperation Main.MyParameter \"Set Value\" --value=\"2.2\"'],\n",
       " ['classoperation Main.MyParameter \"Set Value\" --value=\"2.5\"'],\n",
       " ['classoperation Main.MyParameter \"Set Value\" --value=\"2.7\"'],\n",
       " ['classoperation Main.MyParameter \"Set Value\" --value=\"2.9\"'],\n",
       " ['classoperation Main.MyParameter \"Set Value\" --value=\"3.1\"']]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameter_list = [2.2, 2.5, 2.7, 2.9, 3.1]\n",
    "\n",
    "mg = AnyMacro(SetValue('Main.MyParameter', parameter_list ))\n",
    "mg.create_macros(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A simple parameter study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us combine the previous to create a parameter study. We will continue with the simplified knee model where we left off in the previous tutorial. The parameter study will vary the patella tendon length from 2.0cm to 8.0cm, and observe the effect on maximum muscle activity.\n",
    "\n",
    "First we create a list of patella length parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08]\n"
     ]
    }
   ],
   "source": [
    "patella_tendon_lengths = [\n",
    "    0.02 + i*0.01 \n",
    "    for i in range(7)\n",
    "]\n",
    "print(patella_tendon_lengths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list of values is added to the macros with the `SetValue` class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "macro = [\n",
    "    Load('Knee.any'),\n",
    "    SetValue('Main.MyModel.PatellaLigament.DriverPos', patella_tendon_lengths ),\n",
    "    OperationRun('Main.MyStudy.InverseDynamics'),\n",
    "    Dump('Main.MyStudy.Output.Abscissa.t'),\n",
    "    Dump('Main.MyStudy.Output.MaxMuscleActivity'),\n",
    "    Dump('Main.MyModel.PatellaLigament.DriverPos'),\n",
    "]\n",
    "\n",
    "parameter_study_macro = AnyMacro(macro, number_of_macros= len(patella_tendon_lengths) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now run the model and analyze the resulting maximum muscle activity by plotting the data in the `output` variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c66b897ee8d41febdcecef7385de4a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: 7\n"
     ]
    }
   ],
   "source": [
    "output = app.start_macro(parameter_study_macro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for data in output:\n",
    "    max_activity = data['Main.MyStudy.Output.MaxMuscleActivity']\n",
    "    time = data['Main.MyStudy.Output.Abscissa.t']\n",
    "    patella_ligament_length = data['Main.MyModel.PatellaLigament.DriverPos'][0]\n",
    "    plt.plot(time, max_activity, label='{:.1f} cm'.format(100* patella_ligament_length) )\n",
    "\n",
    "plt.title('Effect of changing patella tendon length')    \n",
    "plt.xlabel('Time steps')\n",
    "plt.ylabel('Max muscle activity')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2);\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "00afe1ba02f541ce9a2b88eecc1f6369": {
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      "model_name": "LayoutModel",
      "state": {}
     },
     "0d24beb4bb6c448c8ad08daee160b953": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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     "11400b7db356447ea8aac81532c4d29b": {
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     "129faf74be434ae5a6a86e415ab146be": {
      "model_module": "@jupyter-widgets/controls",
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      "state": {
       "layout": "IPY_MODEL_9bf5fdef05354997a6b1dd190ec64406",
       "style": "IPY_MODEL_4a01d95b762645e19efde9885e2e15a4",
       "value": " 7/7 [00:05&lt;00:00,  1.51it/s]"
      }
     },
     "17e49e989fbc434aab2750a95cdde67d": {
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